Some of the critical requirements facing semiconductor device manufacturing are continual feature-size reduction, introduction of new materials for higher processing speeds and improved reliability, multilevel metallization (MLM), or interconnections, and increased productivity through larger wafer sizes. Wafer polishing using chemical mechanical planarization (CMP) is a key nanoscale manufacturing step that can significantly impact how the above requirements are met by the industry. However, the increased sophistication of the CMP process has brought difficult manufacturing challenges including the identification of defects such as delamination, dishing, under/over polishing, process monitoring, and process control.
Industrial practices currently used in the art for the identification of delamination defects suggests offline methods such as noncontact capacitance probe measurements, photothermal techniques, and examination of finished wafers under optical and scanning electron microscopy (SEM). However, such offline methods do not provide real-time information that can be used to improve productivity and implement online process monitoring and control. Moreover, offline analysis also increases cost of ownership. Currently there are no efficient in situ tools and detailed implementable procedures known in the art to detect delamination defects.
In recent years, acoustic emission (AE) technology has emerged as a viable means to examine and characterize chemical mechanical planarization (CMP) processes. The AE signal arising out of the CMP process, where material removal takes place at the nanoscale level, has a very high signal to noise ratio and, as such, a high sensitivity. Also, the frequency of the AE signal is very high compared to those of machine vibrations and other environmental noises and, thus, provides a good representation of the material removal process of planarization. The above properties of the AE signal make it very suitable in extracting critical information about the state of the CMP process and in building detection and control strategies.
The acoustic emission signal is nonstationary and its strength, measured in volts, is high at the beginning of the CMP process and subsides as polishing progresses. The signal spans over a broad range of high frequencies and contains an abundance of time-based characteristics and noise. Consequently, visual inspection of time plots of AE signals fails to provide any meaningful information. Thus, to extract meaningful information from the acoustic emission signal, a systematic time-frequency analysis is required.
Acoustic emission signal analysis techniques known in the art include time-domain methods involving descriptors such as peak level, root-mean-square (RMS) value, crest factor, kurtosis analysis, and pulse count. These conventional time-domain analysis methods are sensitive to impulsive oscillations, but have limited utility in extracting hidden patterns and frequency related information in the AE signals. It is known in prior art that this problem is partially overcome by spectral (frequency) analysis such as Fourier transform, the power spectral density, and the coherence function analysis. However, many spectral methods rely on the implicit fundamental assumption of signals being periodic and stationary and are also inefficient in extracting time related features. Moreover, Fourier transform of nonstationary signals results in averaging of the frequency components over the entire duration of the signal. This problem has been addressed to a large extent through the use of short time Fourier transform (STFT) methods. However, this method uses a fixed tiling scheme, i.e., it maintains a constant aspect ratio (the width of the time window to the width of the frequency band) throughout the analysis. As a result, one must choose multiple window widths to analyze different data features localized in time and frequency domains. Hence, the STFT is badly adapted to signals where patterns with different scales appear, and it resolves short time phenomena associated with high frequencies poorly. In recent years, time-frequency methods, such as wavelet-based multiresolution analysis have gained popularity in analysis of both stationary and nonstationary signals. These methods provide excellent time-frequency localized information, which is achieved by varying the aspect ratio. Hence, time and frequency localized features are analyzed simultaneously with high resolution and the scheme is more adaptable to transient signals.
It is known in prior art that wavelet analysis could be used to process acoustic emission but no detailed step-by-step procedure that can implement both in-situ and ex-situ has been presented. Accordingly, what is needed in the art is a systematic wavelet-based time-frequency approach having the potential to analyze nonstationary acoustic emission signals and provide useful insight into the chemical mechanical planarization process for the purpose of identifying defects both online (in-situ) and offline (ex-situ) as required.